Research Prioritisation and Study Design Using Value of Information Analysis

28 June 2026, 8:35 AM – 12:00 PM (local time)

Faculty

  • Jeremy Goldhaber-Fiebert, PhD – Professor of Health Policy, Stanford University, United States
  • Natalia Kunst, PhD – Associate Director Global HEOR, Bristol Myers Squibb, and Adjunct Assistant Professor, Yale School of Medicine, United States

Course Overview

Value of Information (VOI) is a key concept in decision analysis that can be used to determine research priorities, inform resource allocation for potential further research, and design proposed research studies. This course introduces the general concepts behind VOI, presents several key VOI measures, and highlights where they can be most useful in directing future research. It will also demonstrate key graphical presentations of these measures and critically evaluate VOI analyses and their underlying assumptions.

VOI encompasses a suite of measures that quantify the value of reducing parametric uncertainty within a health economic model. These measures can determine whether the current evidence base for a health economic decision model is sufficient to support policy decisions. They can also direct future research by identifying the model inputs with the greatest influence on decision uncertainty. In addition, VOI measures can be used to determine the optimal design for a research study.

Despite this versatility, VOI has rarely been used in practice for research prioritization and study design. This is due to limited familiarity with the methods, difficulties interpreting the measures, concerns about the assumptions underpinning them, and computational complexity. The Collaborative Network for Value of Information (ConVOI) group is an international team of experts in developing and applying cutting-edge VOI methods that aim to address these challenges.

Learning Objectives

Participants will learn how to:

  • Interpret the Expected Value of Perfect Information (EVPI)
  • Interpret the Expected Value of Perfect Partial Information (EVPPI)
  • Interpret the Expected Value of Sample Information (EVSI)
  • Interpret the Expected Net Benefit of Sampling (ENBS)
  • Explore the results of a VOI analysis using graphical displays
  • Use VOI analysis to determine research priorities and design clinical research

Course Format

This half-day course is a mixture of informal lectures and discussion sessions. The lectures will present the definition of key VOI measures, discuss the assumptions underpinning VOI analyses, and demonstrate a number of graphical displays for VOI measures.

Participants will discuss and interpret examples of VOI analyses from the literature. R code will be provided for all examples so that participants can continue to explore what they learn during the course.

Participant Requirements

Participants should have some knowledge of health economic evaluation, health technology assessment, and probabilistic sensitivity analysis. Those who want to follow along with the R code examples should bring their own computers with updated versions of R and RStudio installed., health economics, and policy evaluation who are interested in learning how value of information analysis can support research prioritisation and study design.